Software Testing Evolution
In software development life cycle, software testing has higher importance. Even though developers develop according to requirement, during testing only we make sure that everything meets the expectation. When looking at past decades, it is quite evident that software testing has gradually evolved. At the first stage, testing started only after the software development was completed. Currently, most of the companies have moved to agile processes and that has enabled software testing to be initiated parallel to the development process. Reason behind selecting agile processes is finding bugs as early as possible and fixing those earlier. For that automation testing came to play a role with the manual testing process. In this era, it can be seen that Artificial Intelligence (AI) is stepping out to software testing. It is worth talking about and application of AI to test automation. In this article, we will be taking a look at AI test automation and it will also provide guidance on how to apply it.
What is AI?
AI is also known as machine intelligence. Simply, machines performing natural human intelligence is what’s known as ‘AI’ or Artificial Intelligence. Image Recognition, Speech Recognition, Chatbots, Natural Language Generation etc. came to the world as applications because of AI support.
When looking at the current AI systems, most of them belong to “Limited memory” (based on the past experiences, system/machine reacts). Above mentioned can be also be used in test automation to maintain tests and here will provide directions to apply AI to your project.
Why should you apply AI on Test Automation?
As the survey conducted by testcraft using 200+ testers, followings are the major concerns identified in testing
- Test Maintenance
- Not enough man power
- Lack of integrations
- Want to increase coverage
- Hard to find good test engineers
- Not able to keep up with an agile schedule
Out of all participants, 50% of testers have mentioned that Test Maintenance is the biggest bottleneck for testing. So AI will be the best solution to overcome this issue. Other than the test maintenance, AI supports time saving, stability of tests, finding bugs fastest way and fixing those much faster etc.
AI enhance the software testing efficiency
“Self-healing” is the mechanism that is used in AI to overcome the above mentioned drawbacks. What self-healing does is, it identifies damages, errors by itself and has ability repair those damages and fix errors automatically by itself without human involvement.
The problem in test automation is, once we automated things, if those got failed we have to spend some time to validate it to identify root cause. This will be time taken one. So by patterning this self-healing behavior in to our test automation, mainly it supports error handling and also for information flow management. When occurring errors, AI is able to adjust those by viewing system response and accordingly patterned to self-heal those errors. With this mechanism, followings are the advantages can be gained for testing.
- Able to maintain test stability
- Able to create most reliable automated tests
- Able to identify bugs earlier and resolve those so fast way.
- Able to save time and reduce the cost of failures
- Reduce the maintenance
- Able to follow continues learning with data using and make correct decisions to overcome failures
AI test automation Tools
Following are the currently most popular AI test automation tools
Stay informed about future AI test automation trends
Applying AI for test automation will be a great opportunity for software testing in future since it addressing current test maintain issues without any human involvement. As a result of that testers will not have to go into the code manually to identify issues and to validate those issues. To get the greatest benefit, it is good keeping touch with future AI test automation trends. For that you can follow Blogs, research articles related to AI.
AI in test automation is not an obstacle, but an opportunity